AI-Driven Adaptive Cloud Security Framework for Modern Digital Infrastructures

Abstract

Cloud computing has become the backbone of digital transformation, providing scalable, flexible, and cost-effective infrastructure for enterprises worldwide. However, the dynamic and distributed nature of cloud environments exposes them to complex and evolving security threats that traditional protection mechanisms struggle to manage. In response, artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools for creating adaptive, intelligent, and proactive cloud security systems. This review paper explores the evolution of AI-driven adaptive cloud security frameworks designed to protect modern digital infrastructures. It examines fundamental cloud security models, including the shared responsibility and zero trust paradigms, and discusses prevalent security challenges such as data breaches, insider threats, and distributed denial-of-service (DDoS) attacks. The paper also analyzes how AI techniques particularly machine learning, deep learning, reinforcement learning, and federated learning enhance detection accuracy and automate defense strategies. Furthermore, recent case studies and frameworks are reviewed to highlight advancements in self-healing, automated, and context-aware cloud security systems. Finally, the study identifies key challenges related to data privacy, explainability, adversarial robustness, and scalability while outlining future research directions toward quantum-resilient and autonomous security operations. Overall, this paper provides a comprehensive overview of how AI is transforming cloud security from static, reactive systems into adaptive, intelligent, and self-defending digital ecosystems.